Challenges in forecasting infectious disease outbreaks

IDM conference, 6 November 2024

Sebastian Funk

https://epiforecasts.io

Short-term forecasts can inform decision making

Anticipate healthcare demand

Finger et al., BMC Medicine, 2019

Short-term forecasts can inform decision making

Design interventions

Short-term forecasts can inform decision making

Plan clinical trials

Munday et al., eLife, 2024

What did we learn from COVID-19 forecasts?

Initial efforts faced multiple challenges

1-week ahead forecasts produced by SPI-M for SAGE in the UK.

Funk et al., medRxiv, 2020

Forecast hubs supported systematic collection of forecasts

Reich et al., Am J Public Health, 2022

Median ensemble outperformed individual models

Sherratt et al., eLife, 2023

Case forecasts were poorly calibrated a few weeks from the forecast date

Sherratt et al., eLife, 2023

Unpredictable changes in human behaviour made forecasting harder

Gimma et al., PLOS Medicine, 2022

Measurements of behaviour changes did not improve predictions

Observed behaviour as predictor: improvement of forecasts, but only once age-specific reporting is taken into account.

Munday et al., PLOS Comp Biol, 2023

Variants as predictor improved forecasts during transitions

https://github.com/epiforecasts/forecast.vocs
https://github.com/jbracher/branching_process_delta

Humans were better than models at predicting cases, but not deaths

Bosse et al., PLOS Comp Biol, 2022

No clear pattern of which model performed best

Sherratt et al., in progress

What can we conclude for the next pandemic?

For forecasts to be available when it most matters we need models and infrastructure ready to be deployed.

Charniga et al., Epidemics, 2024

(for more on mpox forecasting see also Will Green’s poster)

Any model of the future is a prediction and can be evaluated as such.

Howerton et al., Nat Comm, 2023

More work is needed to determine which data and methods for forecasting best support public health.

Slides at

https://epiforecasts.io/slides/idm_20241106.html